In existing intelligent processing solutions for video data, various IP cameras (IPCs) collect video data and send a video stream to a network storage server to store the same.
Then, users can read the video data from the storage server for playing. During the playing, feature requirements may be set to extract, in combination with intelligent information, video data satisfying the requirements for playing, thereby improving video search efficiency and realizing intelligent processing. The feature requirements are feature information of moving objects in an image, such as all the video data of images in which there are moving vehicles.
The existing intelligent processing solutions for video data are achieved specifically by means of:
configuring an individual video analyzing server, which sets a feature rule, such as data of moving vehicles present in all of the video images. After each IPC stores video streams in a storage server, the video analyzing server reads video data from the storage server periodically for analysis, and generates intelligent information and stores the same when the feature rule is satisfied; parameters of the feature rule satisfied by respective video data are recorded in the intelligent information. Next, when playing the video data in combination with the feature rule, it is possible to determine the video data satisfying the requirements according to the intelligent information, so as to play the video.
In the existing solutions, a post-analyzing method is used, wherein the periodical analysis of intelligent data is performed after the IPC stores video streams in a storage server, thereby only history stream can be processed. Due to the fact that IPC stores data periodically and not in real time, the video data which have been collected currently by an IPC but have not been stored in the storage server cannot be analyzed. Moreover, an individual video analyzing server completes the analysis for all the IPC video streams, which imposes a large amount of work and is time-consuming, and thus increasing technical difficulties for the video analyzing server. Therefore, the existing solutions of storing video data and intelligent data have a defect of poor time effectiveness.